{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/meta-learning/papers/13","list_of":"/task/meta-learning","task":"Meta-Learning","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":13,"pages_in_order":36,"rows_per_page":100,"rows":[1201,1300],"of":3569,"counts":{"archive_papers_tagged":3569,"with_a_code_link":1408,"where_syntology_ran_a_sample":420,"not_listed_spam_title":0,"listed":3569,"listed_where_code_ran":420,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":353,"every_run_a_failure_of_syntologys_instrument":67,"listed_with_a_run_with_no_instrument_failure":353,"listed_every_run_a_failure_of_syntologys_instrument":67,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/meta-learning","prev":"/task/meta-learning/papers/12","next":"/task/meta-learning/papers/14","papers":[{"url":"/paper/an-analysis-of-the-adaptation-speed-of-causal","slug":"an-analysis-of-the-adaptation-speed-of-causal","title":"An Analysis of the Adaptation Speed of Causal Models","date":"2020-05-18","arxiv_id":"2005.09136","repositories_listed":1,"syntology":null},{"url":"/paper/bayesian-meta-sampling-for-fast-uncertainty","slug":"bayesian-meta-sampling-for-fast-uncertainty","title":"Bayesian Meta Sampling for Fast Uncertainty Adaptation","date":"2020-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/towards-fast-adaptation-of-neural","slug":"towards-fast-adaptation-of-neural","title":"Towards Fast Adaptation of Neural Architectures with Meta Learning","date":"2020-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/bayesian-online-meta-learning-with-laplace","slug":"bayesian-online-meta-learning-with-laplace","title":"Addressing Catastrophic Forgetting in Few-Shot Problems","date":"2020-04-30","arxiv_id":"2005.00146","repositories_listed":1,"syntology":null},{"url":"/paper/reinforcement-meta-learning-for-interception","slug":"reinforcement-meta-learning-for-interception","title":"Reinforcement Meta-Learning for Interception of Maneuvering Exoatmospheric Targets with Parasitic Attitude Loop","date":"2020-04-18","arxiv_id":"2004.09978","repositories_listed":1,"syntology":null},{"url":"/paper/meta-meta-classification-for-one-shot","slug":"meta-meta-classification-for-one-shot","title":"Meta-Meta Classification for One-Shot Learning","date":"2020-04-17","arxiv_id":"2004.08083","repositories_listed":1,"syntology":null},{"url":"/paper/regularizing-meta-learning-via-gradient","slug":"regularizing-meta-learning-via-gradient","title":"Regularizing Meta-Learning via Gradient Dropout","date":"2020-04-13","arxiv_id":"2004.05859","repositories_listed":1,"syntology":null},{"url":"/paper/meta-learning-in-neural-networks-a-survey","slug":"meta-learning-in-neural-networks-a-survey","title":"Meta-Learning in Neural Networks: A Survey","date":"2020-04-11","arxiv_id":"2004.05439","repositories_listed":1,"syntology":null},{"url":"/paper/metaiqa-deep-meta-learning-for-no-reference","slug":"metaiqa-deep-meta-learning-for-no-reference","title":"MetaIQA: Deep Meta-learning for No-Reference Image Quality Assessment","date":"2020-04-11","arxiv_id":"2004.05508","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/metaiqa-deep-meta-learning-for-no-reference#ran","syntology_url":"https://syntology.ai/paper/2004.05508","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.05508"}},"official":null}},{"url":"/paper/metasleeplearner-fast-adaptation-of-bio","slug":"metasleeplearner-fast-adaptation-of-bio","title":"MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning","date":"2020-04-08","arxiv_id":"2004.04157","repositories_listed":1,"syntology":null},{"url":"/paper/meta-learning-for-short-utterance-speaker","slug":"meta-learning-for-short-utterance-speaker","title":"Meta-Learning for Short Utterance Speaker Recognition with Imbalance Length Pairs","date":"2020-04-06","arxiv_id":"2004.02863","repositories_listed":1,"syntology":null},{"url":"/paper/there-and-back-again-revisiting","slug":"there-and-back-again-revisiting","title":"There and Back Again: Revisiting Backpropagation Saliency Methods","date":"2020-04-06","arxiv_id":"2004.02866","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/there-and-back-again-revisiting#ran","syntology_url":"https://syntology.ai/paper/2004.02866","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.02866"}},"official":{"repos":["srebuffi/revisiting_saliency"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/arbitrary-scale-super-resolution-for-brain","slug":"arbitrary-scale-super-resolution-for-brain","title":"Arbitrary Scale Super-Resolution for Brain MRI Images","date":"2020-04-05","arxiv_id":"2004.02086","repositories_listed":1,"syntology":null},{"url":"/paper/scene-adaptive-video-frame-interpolation-via","slug":"scene-adaptive-video-frame-interpolation-via","title":"Scene-Adaptive Video Frame Interpolation via Meta-Learning","date":"2020-04-02","arxiv_id":"2004.00779","repositories_listed":1,"syntology":null},{"url":"/paper/dpgn-distribution-propagation-graph-network","slug":"dpgn-distribution-propagation-graph-network","title":"DPGN: Distribution Propagation Graph Network for Few-shot Learning","date":"2020-03-31","arxiv_id":"2003.14247","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-learn-single-domain","slug":"learning-to-learn-single-domain","title":"Learning to Learn Single Domain Generalization","date":"2020-03-30","arxiv_id":"2003.13216","repositories_listed":1,"syntology":null},{"url":"/paper/multi-task-reinforcement-learning-with-soft","slug":"multi-task-reinforcement-learning-with-soft","title":"Multi-Task Reinforcement Learning with Soft Modularization","date":"2020-03-30","arxiv_id":"2003.13661","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-optimization-dynamics-of-wide","slug":"on-the-optimization-dynamics-of-wide","title":"On Infinite-Width Hypernetworks","date":"2020-03-27","arxiv_id":"2003.12193","repositories_listed":1,"syntology":null},{"url":"/paper/instance-credibility-inference-for-few-shot","slug":"instance-credibility-inference-for-few-shot","title":"Instance Credibility Inference for Few-Shot Learning","date":"2020-03-26","arxiv_id":"2003.11853","repositories_listed":1,"syntology":null},{"url":"/paper/itaml-an-incremental-task-agnostic-meta","slug":"itaml-an-incremental-task-agnostic-meta","title":"iTAML: An Incremental Task-Agnostic Meta-learning Approach","date":"2020-03-25","arxiv_id":"2003.11652","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-class-balanced-methods-for-long","slug":"rethinking-class-balanced-methods-for-long","title":"Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective","date":"2020-03-24","arxiv_id":"2003.10780","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/rethinking-class-balanced-methods-for-long#ran","syntology_url":"https://syntology.ai/paper/2003.10780","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.10780"}},"official":{"repos":["abdullahjamal/Longtail_DA"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/goal-conditioned-end-to-end-visuomotor","slug":"goal-conditioned-end-to-end-visuomotor","title":"Goal-Conditioned End-to-End Visuomotor Control for Versatile Skill Primitives","date":"2020-03-19","arxiv_id":"2003.08854","repositories_listed":1,"syntology":null},{"url":"/paper/semi-modular-inference-enhanced-learning-in","slug":"semi-modular-inference-enhanced-learning-in","title":"Semi-Modular Inference: enhanced learning in multi-modular models by tempering the influence of components","date":"2020-03-15","arxiv_id":"2003.06804","repositories_listed":1,"syntology":null},{"url":"/paper/learning-compositional-rules-via-neural","slug":"learning-compositional-rules-via-neural","title":"Learning Compositional Rules via Neural Program Synthesis","date":"2020-03-12","arxiv_id":"2003.05562","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":1,"n_ran_checked":1,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":5,"phrase":"4 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/learning-compositional-rules-via-neural#ran","syntology_url":"https://syntology.ai/paper/2003.05562","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.05562"}},"official":{"repos":["mtensor/rulesynthesis"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/meta-learning-initializations-for-low","slug":"meta-learning-initializations-for-low","title":"Meta-Learning Initializations for Low-Resource Drug Discovery","date":"2020-03-12","arxiv_id":"2003.05996","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/meta-learning-initializations-for-low#ran","syntology_url":"https://syntology.ai/paper/2003.05996","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.05996"}},"official":{"repos":["GSK-AI/meta-learning-qsar"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/online-fast-adaptation-and-knowledge","slug":"online-fast-adaptation-and-knowledge","title":"Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning","date":"2020-03-12","arxiv_id":"2003.05856","repositories_listed":1,"syntology":{"n":7,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/online-fast-adaptation-and-knowledge#ran","syntology_url":"https://syntology.ai/paper/2003.05856","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.05856"}},"official":{"repos":["ElementAI/osaka"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/meta-learning-curiosity-algorithms-1","slug":"meta-learning-curiosity-algorithms-1","title":"Meta-learning curiosity algorithms","date":"2020-03-11","arxiv_id":"2003.05325","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/meta-learning-curiosity-algorithms-1#ran","syntology_url":"https://syntology.ai/paper/2003.05325","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.05325"}},"official":{"repos":["mfranzs/meta-learning-curiosity-algorithms"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/online-meta-critic-learning-for-off-policy-1","slug":"online-meta-critic-learning-for-off-policy-1","title":"Online Meta-Critic Learning for Off-Policy Actor-Critic Methods","date":"2020-03-11","arxiv_id":"2003.05334","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":6,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":6,"phrase":"6 ran (of which 6 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 6 samples that ran constructed an object rather than computing a result","sample_list":"/paper/online-meta-critic-learning-for-off-policy-1#ran","syntology_url":"https://syntology.ai/paper/2003.05334","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.05334"}},"official":{"repos":["zwfightzw/Meta-Critic"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":6,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fast-online-adaptation-in-robotics-through","slug":"fast-online-adaptation-in-robotics-through","title":"Fast Online Adaptation in Robotics through Meta-Learning Embeddings of Simulated Priors","date":"2020-03-10","arxiv_id":"2003.04663","repositories_listed":1,"syntology":null},{"url":"/paper/learning-state-dependent-losses-for-inverse","slug":"learning-state-dependent-losses-for-inverse","title":"Learning State-Dependent Losses for Inverse Dynamics Learning","date":"2020-03-10","arxiv_id":"2003.04947","repositories_listed":1,"syntology":null},{"url":"/paper/pac-bayesian-meta-learning-with-implicit","slug":"pac-bayesian-meta-learning-with-implicit","title":"PAC-Bayes meta-learning with implicit task-specific posteriors","date":"2020-03-05","arxiv_id":"2003.02455","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/pac-bayesian-meta-learning-with-implicit#ran","syntology_url":"https://syntology.ai/paper/2003.02455","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.02455"}},"official":{"repos":["cnguyen10/few_shot_meta_learning"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/zero-shot-cross-lingual-transfer-with-meta","slug":"zero-shot-cross-lingual-transfer-with-meta","title":"Zero-Shot Cross-Lingual Transfer with Meta Learning","date":"2020-03-05","arxiv_id":"2003.02739","repositories_listed":1,"syntology":null},{"url":"/paper/end-to-end-fast-training-of-communication","slug":"end-to-end-fast-training-of-communication","title":"End-to-End Fast Training of Communication Links Without a Channel Model via Online Meta-Learning","date":"2020-03-03","arxiv_id":"2003.01479","repositories_listed":1,"syntology":null},{"url":"/paper/transductive-few-shot-learning-with-meta","slug":"transductive-few-shot-learning-with-meta","title":"Meta-Learned Confidence for Few-shot Learning","date":"2020-02-27","arxiv_id":"2002.12017","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-monte-carlo-meta-learning-of","slug":"adversarial-monte-carlo-meta-learning-of","title":"Adversarial Monte Carlo Meta-Learning of Optimal Prediction Procedures","date":"2020-02-26","arxiv_id":"2002.11275","repositories_listed":1,"syntology":null},{"url":"/paper/provable-meta-learning-of-linear","slug":"provable-meta-learning-of-linear","title":"Provable Meta-Learning of Linear Representations","date":"2020-02-26","arxiv_id":"2002.11684","repositories_listed":1,"syntology":null},{"url":"/paper/a-structured-prediction-approach-for-2","slug":"a-structured-prediction-approach-for-2","title":"Structured Prediction for Conditional Meta-Learning","date":"2020-02-20","arxiv_id":"2002.08799","repositories_listed":1,"syntology":null},{"url":"/paper/meta-learning-extractors-for-music-source","slug":"meta-learning-extractors-for-music-source","title":"Meta-learning Extractors for Music Source Separation","date":"2020-02-17","arxiv_id":"2002.07016","repositories_listed":1,"syntology":null},{"url":"/paper/unraveling-meta-learning-understanding","slug":"unraveling-meta-learning-understanding","title":"Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks","date":"2020-02-17","arxiv_id":"2002.06753","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/unraveling-meta-learning-understanding#ran","syntology_url":"https://syntology.ai/paper/2002.06753","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.06753"}},"official":{"repos":["goldblum/FeatureClustering"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/provably-convergent-policy-gradient-methods","slug":"provably-convergent-policy-gradient-methods","title":"On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement Learning","date":"2020-02-12","arxiv_id":"2002.05135","repositories_listed":1,"syntology":null},{"url":"/paper/local-nonparametric-meta-learning","slug":"local-nonparametric-meta-learning","title":"Local Nonparametric Meta-Learning","date":"2020-02-09","arxiv_id":"2002.03272","repositories_listed":1,"syntology":null},{"url":"/paper/task-augmentation-by-rotating-for-meta","slug":"task-augmentation-by-rotating-for-meta","title":"Task Augmentation by Rotating for Meta-Learning","date":"2020-02-08","arxiv_id":"2003.00804","repositories_listed":1,"syntology":null},{"url":"/paper/geometric-dataset-distances-via-optimal","slug":"geometric-dataset-distances-via-optimal","title":"Geometric Dataset Distances via Optimal Transport","date":"2020-02-07","arxiv_id":"2002.02923","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-scene-adaptive-crowd-counting-using","slug":"few-shot-scene-adaptive-crowd-counting-using","title":"Few-Shot Scene Adaptive Crowd Counting Using Meta-Learning","date":"2020-02-01","arxiv_id":"2002.00264","repositories_listed":1,"syntology":null},{"url":"/paper/extreme-algorithm-selection-with-dyadic","slug":"extreme-algorithm-selection-with-dyadic","title":"Extreme Algorithm Selection With Dyadic Feature Representation","date":"2020-01-29","arxiv_id":"2001.10741","repositories_listed":1,"syntology":null},{"url":"/paper/fast-adaptation-to-super-resolution-networks","slug":"fast-adaptation-to-super-resolution-networks","title":"Fast Adaptation to Super-Resolution Networks via Meta-Learning","date":"2020-01-09","arxiv_id":"2001.02905","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/fast-adaptation-to-super-resolution-networks#ran","syntology_url":"https://syntology.ai/paper/2001.02905","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2001.02905"}},"official":{"repos":["parkseobin/MLSR"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/frosting-weights-for-better-continual","slug":"frosting-weights-for-better-continual","title":"Frosting Weights for Better Continual Training","date":"2020-01-07","arxiv_id":"2001.01829","repositories_listed":1,"syntology":null},{"url":"/paper/from-learning-to-meta-learning-reduced","slug":"from-learning-to-meta-learning-reduced","title":"From Learning to Meta-Learning: Reduced Training Overhead and Complexity for Communication Systems","date":"2020-01-05","arxiv_id":"2001.01227","repositories_listed":1,"syntology":null},{"url":"/paper/automated-relational-meta-learning-1","slug":"automated-relational-meta-learning-1","title":"Automated Relational Meta-learning","date":"2020-01-03","arxiv_id":"2001.00745","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"0 ran · 2 unverified","sample_list":"/paper/automated-relational-meta-learning-1#ran","syntology_url":"https://syntology.ai/paper/2001.00745","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2001.00745"}},"official":{"repos":["huaxiuyao/ARML"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/deep-transfer-learning-based-downlink-channel","slug":"deep-transfer-learning-based-downlink-channel","title":"Deep Transfer Learning Based Downlink Channel Prediction for FDD Massive MIMO Systems","date":"2019-12-27","arxiv_id":"1912.12265","repositories_listed":1,"syntology":null},{"url":"/paper/variational-metric-scaling-for-metric-based","slug":"variational-metric-scaling-for-metric-based","title":"Variational Metric Scaling for Metric-Based Meta-Learning","date":"2019-12-26","arxiv_id":"1912.11809","repositories_listed":1,"syntology":null},{"url":"/paper/automl-exploration-vs-exploitation","slug":"automl-exploration-vs-exploitation","title":"AutoML: Exploration v.s. Exploitation","date":"2019-12-23","arxiv_id":"1912.10746","repositories_listed":1,"syntology":null},{"url":"/paper/meta-graph-few-shot-link-prediction-via-meta-1","slug":"meta-graph-few-shot-link-prediction-via-meta-1","title":"Meta-Graph: Few Shot Link Prediction via Meta Learning","date":"2019-12-20","arxiv_id":"1912.09867","repositories_listed":1,"syntology":null},{"url":"/paper/meta-learning-initializations-for-image-1","slug":"meta-learning-initializations-for-image-1","title":"Meta-Learning Initializations for Image Segmentation","date":"2019-12-13","arxiv_id":"1912.06290","repositories_listed":1,"syntology":null},{"url":"/paper/meta-learning-without-memorization-1","slug":"meta-learning-without-memorization-1","title":"Meta-Learning without Memorization","date":"2019-12-09","arxiv_id":"1912.03820","repositories_listed":1,"syntology":null},{"url":"/paper/metafun-meta-learning-with-iterative","slug":"metafun-meta-learning-with-iterative","title":"MetaFun: Meta-Learning with Iterative Functional Updates","date":"2019-12-05","arxiv_id":"1912.02738","repositories_listed":1,"syntology":null},{"url":"/paper/badger-learning-to-learn-learning-algorithms","slug":"badger-learning-to-learn-learning-algorithms","title":"BADGER: Learning to (Learn [Learning Algorithms] through Multi-Agent Communication)","date":"2019-12-03","arxiv_id":"1912.01513","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-learn-by-self-critique-1","slug":"learning-to-learn-by-self-critique-1","title":"Learning to Learn By Self-Critique","date":"2019-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/online-within-online-meta-learning","slug":"online-within-online-meta-learning","title":"Online-Within-Online Meta-Learning","date":"2019-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/sparsely-grouped-input-variables-for-neural","slug":"sparsely-grouped-input-variables-for-neural","title":"Sparsely Grouped Input Variables for Neural Networks","date":"2019-11-29","arxiv_id":"1911.13068","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-learn-words-from-narrated-video","slug":"learning-to-learn-words-from-narrated-video","title":"Learning to Learn Words from Visual Scenes","date":"2019-11-25","arxiv_id":"1911.11237","repositories_listed":1,"syntology":null},{"url":"/paper/regularized-fine-grained-meta-face-anti","slug":"regularized-fine-grained-meta-face-anti","title":"Regularized Fine-grained Meta Face Anti-spoofing","date":"2019-11-25","arxiv_id":"1911.10771","repositories_listed":1,"syntology":null},{"url":"/paper/deep-tile-coder-an-efficient-sparse","slug":"deep-tile-coder-an-efficient-sparse","title":"Fuzzy Tiling Activations: A Simple Approach to Learning Sparse Representations Online","date":"2019-11-19","arxiv_id":"1911.08068","repositories_listed":1,"syntology":null},{"url":"/paper/constructing-multiple-tasks-for-augmentation","slug":"constructing-multiple-tasks-for-augmentation","title":"Constructing Multiple Tasks for Augmentation: Improving Neural Image Classification With K-means Features","date":"2019-11-18","arxiv_id":"1911.07518","repositories_listed":1,"syntology":null},{"url":"/paper/meta-reinforced-synthetic-data-for-one-shot-1","slug":"meta-reinforced-synthetic-data-for-one-shot-1","title":"Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition","date":"2019-11-17","arxiv_id":"1911.07164","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/meta-reinforced-synthetic-data-for-one-shot-1#ran","syntology_url":"https://syntology.ai/paper/1911.07164","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.07164"}},"official":{"repos":["apple2373/MetaIRNet"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/enhanced-meta-learning-for-cross-lingual","slug":"enhanced-meta-learning-for-cross-lingual","title":"Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resources","date":"2019-11-14","arxiv_id":"1911.06161","repositories_listed":1,"syntology":null},{"url":"/paper/meta-label-correction-for-learning-with-weak-1","slug":"meta-label-correction-for-learning-with-weak-1","title":"Meta Label Correction for Noisy Label Learning","date":"2019-11-10","arxiv_id":"1911.03809","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/meta-label-correction-for-learning-with-weak-1#ran","syntology_url":"https://syntology.ai/paper/1911.03809","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.03809"}},"official":{"repos":["microsoft/mlc"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-one-shot-imitation-from-humans","slug":"learning-one-shot-imitation-from-humans","title":"Learning One-Shot Imitation from Humans without Humans","date":"2019-11-04","arxiv_id":"1911.01103","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-customize-language-model-for","slug":"learning-to-customize-language-model-for","title":"Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks","date":"2019-10-31","arxiv_id":"1910.14326","repositories_listed":1,"syntology":null},{"url":"/paper/decoupling-adaptation-from-modeling-with-meta-1","slug":"decoupling-adaptation-from-modeling-with-meta-1","title":"When MAML Can Adapt Fast and How to Assist When It Cannot","date":"2019-10-30","arxiv_id":"1910.13603","repositories_listed":1,"syntology":null},{"url":"/paper/hidra-head-initialization-across-dynamic","slug":"hidra-head-initialization-across-dynamic","title":"HIDRA: Head Initialization across Dynamic targets for Robust Architectures","date":"2019-10-28","arxiv_id":"1910.12749","repositories_listed":1,"syntology":null},{"url":"/paper/meta-learning-with-dynamic-memory-based","slug":"meta-learning-with-dynamic-memory-based","title":"Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection","date":"2019-10-25","arxiv_id":"1910.11621","repositories_listed":1,"syntology":null},{"url":"/paper/speaker-adaptive-training-using-model","slug":"speaker-adaptive-training-using-model","title":"Speaker Adaptive Training using Model Agnostic Meta-Learning","date":"2019-10-23","arxiv_id":"1910.10605","repositories_listed":1,"syntology":null},{"url":"/paper/bottom-up-meta-policy-search","slug":"bottom-up-meta-policy-search","title":"Bottom-Up Meta-Policy Search","date":"2019-10-22","arxiv_id":"1910.10232","repositories_listed":1,"syntology":null},{"url":"/paper/meta-learning-to-communicate-fast-end-to-end","slug":"meta-learning-to-communicate-fast-end-to-end","title":"Meta-Learning to Communicate: Fast End-to-End Training for Fading Channels","date":"2019-10-22","arxiv_id":"1910.09945","repositories_listed":1,"syntology":null},{"url":"/paper/exploration-via-sample-efficient-subgoal","slug":"exploration-via-sample-efficient-subgoal","title":"Dynamic Subgoal-based Exploration via Bayesian Optimization","date":"2019-10-21","arxiv_id":"1910.09143","repositories_listed":1,"syntology":null},{"url":"/paper/meta-transfer-learning-through-hard-tasks","slug":"meta-transfer-learning-through-hard-tasks","title":"Meta-Transfer Learning through Hard Tasks","date":"2019-10-07","arxiv_id":"1910.03648","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/meta-transfer-learning-through-hard-tasks#ran","syntology_url":"https://syntology.ai/paper/1910.03648","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.03648"}},"official":{"repos":["yaoyao-liu/meta-transfer-learning"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/robust-few-shot-learning-with-adversarially-1","slug":"robust-few-shot-learning-with-adversarially-1","title":"Adversarially Robust Few-Shot Learning: A Meta-Learning Approach","date":"2019-10-02","arxiv_id":"1910.00982","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/robust-few-shot-learning-with-adversarially-1#ran","syntology_url":"https://syntology.ai/paper/1910.00982","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.00982"}},"official":{"repos":["goldblum/AdversarialQuerying"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/meta-r-cnn-towards-general-solver-for-1","slug":"meta-r-cnn-towards-general-solver-for-1","title":"Meta R-CNN: Towards General Solver for Instance-Level Low-Shot Learning","date":"2019-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/chameleon-learning-model-initializations","slug":"chameleon-learning-model-initializations","title":"Chameleon: Learning Model Initializations Across Tasks With Different Schemas","date":"2019-09-30","arxiv_id":"1909.13576","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/chameleon-learning-model-initializations#ran","syntology_url":"https://syntology.ai/paper/1909.13576","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.13576"}},"official":{"repos":["radrumond/Chameleon"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/meta-learning-algorithms-for-few-shot","slug":"meta-learning-algorithms-for-few-shot","title":"Meta-learning algorithms for Few-Shot Computer Vision","date":"2019-09-30","arxiv_id":"1909.13579","repositories_listed":1,"syntology":null},{"url":"/paper/learning-fast-adaptation-with-meta-strategy","slug":"learning-fast-adaptation-with-meta-strategy","title":"Learning Fast Adaptation with Meta Strategy Optimization","date":"2019-09-28","arxiv_id":"1909.12995","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-fast-adaptation-with-meta-strategy#ran","syntology_url":"https://syntology.ai/paper/1909.12995","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.12995"}},"official":null}},{"url":"/paper/decoder-choice-network-for-meta-learning","slug":"decoder-choice-network-for-meta-learning","title":"Decoder Choice Network for Meta-Learning","date":"2019-09-25","arxiv_id":"1909.11446","repositories_listed":1,"syntology":null},{"url":"/paper/decoupling-adaptation-from-modeling-with-meta","slug":"decoupling-adaptation-from-modeling-with-meta","title":"Decoupling Adaptation from Modeling with Meta-Optimizers for Meta Learning","date":"2019-09-25","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/es-maml-simple-hessian-free-meta-learning","slug":"es-maml-simple-hessian-free-meta-learning","title":"ES-MAML: Simple Hessian-Free Meta Learning","date":"2019-09-25","arxiv_id":"1910.01215","repositories_listed":1,"syntology":null},{"url":"/paper/tackling-long-tailed-relations-and-uncommon","slug":"tackling-long-tailed-relations-and-uncommon","title":"Tackling Long-Tailed Relations and Uncommon Entities in Knowledge Graph Completion","date":"2019-09-25","arxiv_id":"1909.11359","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/tackling-long-tailed-relations-and-uncommon#ran","syntology_url":"https://syntology.ai/paper/1909.11359","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.11359"}},"official":{"repos":["ZihaoWang/Few-shot-KGC"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/meta-neighborhoods","slug":"meta-neighborhoods","title":"Meta-Neighborhoods","date":"2019-09-18","arxiv_id":"1909.09140","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-propagate-for-graph-meta-learning","slug":"learning-to-propagate-for-graph-meta-learning","title":"Learning to Propagate for Graph Meta-Learning","date":"2019-09-11","arxiv_id":"1909.05024","repositories_listed":1,"syntology":null},{"url":"/paper/a-meta-learning-framework-for-generalized","slug":"a-meta-learning-framework-for-generalized","title":"A Meta-Learning Framework for Generalized Zero-Shot Learning","date":"2019-09-10","arxiv_id":"1909.04344","repositories_listed":1,"syntology":null},{"url":"/paper/meta-learnt-priors-slow-down-catastrophic","slug":"meta-learnt-priors-slow-down-catastrophic","title":"Meta-learnt priors slow down catastrophic forgetting in neural networks","date":"2019-09-09","arxiv_id":"1909.04170","repositories_listed":1,"syntology":null},{"url":"/paper/eliminating-bias-in-recommender-systems-via","slug":"eliminating-bias-in-recommender-systems-via","title":"Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback","date":"2019-09-08","arxiv_id":"1910.01444","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/eliminating-bias-in-recommender-systems-via#ran","syntology_url":"https://syntology.ai/paper/1910.01444","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.01444"}},"official":{"repos":["usaito/asymmetric-tri-rec-real"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/meta-learning-with-relational-information-for","slug":"meta-learning-with-relational-information-for","title":"Meta Learning with Relational Information for Short Sequences","date":"2019-09-04","arxiv_id":"1909.02105","repositories_listed":1,"syntology":null},{"url":"/paper/adapting-meta-knowledge-graph-information-for","slug":"adapting-meta-knowledge-graph-information-for","title":"Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot Relations","date":"2019-08-30","arxiv_id":"1908.11513","repositories_listed":1,"syntology":null},{"url":"/paper/meta-learning-with-warped-gradient-descent","slug":"meta-learning-with-warped-gradient-descent","title":"Meta-Learning with Warped Gradient Descent","date":"2019-08-30","arxiv_id":"1909.00025","repositories_listed":1,"syntology":{"n":16,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":0,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/meta-learning-with-warped-gradient-descent#ran","syntology_url":"https://syntology.ai/paper/1909.00025","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.00025"}},"official":{"repos":["flennerhag/warpgrad"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/tgg-transferable-graph-generation-for-zero","slug":"tgg-transferable-graph-generation-for-zero","title":"TGG: Transferable Graph Generation for Zero-shot and Few-shot Learning","date":"2019-08-30","arxiv_id":"1908.11503","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-theory-review-an-optimal","slug":"deep-learning-theory-review-an-optimal","title":"Deep Learning Theory Review: An Optimal Control and Dynamical Systems Perspective","date":"2019-08-28","arxiv_id":"1908.10920","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/deep-learning-theory-review-an-optimal#ran","syntology_url":"https://syntology.ai/paper/1908.10920","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.10920"}},"official":{"repos":["ghliu/mean-field-fcdnn"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/fairness-warnings-and-fair-maml-learning","slug":"fairness-warnings-and-fair-maml-learning","title":"Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data","date":"2019-08-24","arxiv_id":"1908.09092","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-demodulate-from-few-pilots-via","slug":"learning-to-demodulate-from-few-pilots-via","title":"Learning to Demodulate from Few Pilots via Offline and Online Meta-Learning","date":"2019-08-23","arxiv_id":"1908.09049","repositories_listed":1,"syntology":null},{"url":"/paper/metaadvdet-towards-robust-detection-of","slug":"metaadvdet-towards-robust-detection-of","title":"MetaAdvDet: Towards Robust Detection of Evolving Adversarial Attacks","date":"2019-08-06","arxiv_id":"1908.02199","repositories_listed":1,"syntology":null},{"url":"/paper/melu-meta-learned-user-preference-estimator","slug":"melu-meta-learned-user-preference-estimator","title":"MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation","date":"2019-07-31","arxiv_id":"1908.00413","repositories_listed":1,"syntology":null}],"record_sha256":"050c9d5c87230ef3bdf2c8190d9c7f92e7706beb0bd5f01181d7752e5a2dab24","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}